System and method for detecting abnormal respiration via respiratory parameters derived from intracardiac electrogram signals

ABSTRACT

Techniques are provided for tracking patient respiration based upon intracardiac electrogram (IEGM) signals or other electrical cardiac signals. Briefly, respiration patterns are detected based upon cycle-to-cycle changes in morphological features associated with individual electrical events with the IEGM signals. For example, slight changes in the peak amplitudes of QRS-complexes, P-waves or T-waves are tracked to identify cyclical variations representative of patient respiration. Alternatively, the integrals of the morphological features of the individual events may be calculated for use in tracking respiration. In any case, once respiration patterns have been identified, episodes of abnormal respiration, such as apnea, hyperpnea, nocturnal asthma, or the like, may be detected and therapy automatically delivered. In particular, techniques for detecting abnormal respiration based on various respiratory parameters derived from the IEGM—such as respiration depth and respiration power—are described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/127,389, filed May 11, 2005, entitled “System and Method forDetection of Respiration Patterns via Intracardiac Electrogram Signals”,which claimed the benefit of U.S. Provisional Patent Application Ser.No. 60/631,111, filed Nov. 24, 2004.

FIELD OF THE INVENTION

The invention generally relates to implantable medical devices, such aspacemakers or implantable cardioverter/defibrillators (ICDs), and inparticular, to techniques for detecting respiration patterns within apatient in which a medical device is implanted, including abnormalrespiration patterns such as apnea, hypopnea or nocturnal asthma.

BACKGROUND OF THE INVENTION

It is highly desirable to reliably track respiration within patientshaving pacemakers and ICDs. Tracking patient respiration permitspotentially dangerous respiratory disorders, such as apnea, hypopnea,hyperpnea, nocturnal asthma, and Cheyne-Stokes Respiration (CSR), to bedetected. Apnea and hypopnea are abnormal respiration patternscharacterized by periods of significantly reduced respiration. Withhypopnea, respiration is reduced but still present. With apnea, however,respiration may cease completely for 10 seconds or longer. One commonform of apnea is sleep apnea, in which hundreds of individual episodesof apnea can occur during a single night. Accordingly, patients withsleep apnea experience frequent wakefulness at night and excessivesleepiness during the day. In addition, apnea can exacerbate variousmedical conditions, particularly congestive heart failure (CHF) whereinthe patient suffers from poor cardiac function. Indeed, the aberrantblood chemistry levels occurring during sleep apnea are a significantproblem for patients with CHF. Due to poor cardiac function caused byCHF, patients already suffer from generally low blood oxygen levels.Frequent periods of sleep apnea result in even lower blood oxygenlevels.

Episodes of apnea can also occur during CSR, which is an abnormalrespiratory pattern often occurring in patients with CHF. CSR ischaracterized by alternating periods of hypopnea and hyperpnea (i.e.fast, deep breathing.) Briefly, CSR arises principally due to a time lagbetween blood CO₂ levels sensed by the respiratory control nerve centersof the brain and the blood CO₂ levels. With CHF, poor cardiac functionresults in poor blood flow to the brain such that respiratory controlnerve centers respond to blood CO₂ levels that are no longer properlyrepresentative of the overall blood CO₂ levels in the body. Hence, therespiratory control nerve centers trigger an increase in the depth andfrequency of breathing in an attempt to compensate for perceived highblood CO₂ levels—although the blood CO₂ levels have already dropped. Bythe time the respiratory control nerve centers detect the drop in bloodCO₂ levels and act to slow respiration, the blood CO₂ levels havealready increased. This cycle becomes increasingly unbalanced untilrespiration alternates between hypopnea and hyperpnea. The periods ofhypopnea often become sufficiently severe that no breathing occursbetween the periods of hyperpnea, i.e. periods of frank apnea occurbetween the periods of hyperpnea. The wildly fluctuating blood chemistrylevels caused by alternating between hyperpnea and apnea/hypopnea cansignificantly exacerbate CHF and other medical conditions. When CHF isstill mild, CSR usually occurs, if at all, only while the patient issleeping. When it becomes more severe, CSR can occur while the patientis awake.

Abnormal respiration during sleep may also arise due to nocturnalasthma. With asthma, the linings of the airways swell and become moreinflamed. Mucus clogs the airways and the muscles around the airwaystighten and narrow. Hence, breathing becomes difficult and stressful.During an asthma attack, rapid breathing patterns similar to hyperpneaoccur, though little or no oxygen actual reaches the lungs. An asthmaattack may be triggered by allergens, respiratory infections, cold anddry air, or even heartburn. The majority of asthma attacks occur duringthe night, between 3:00 a.m. and 5:00 a.m. Nocturnal asthma has beenassociated with factors such as decreased pulmonary function, hypoxemiaand circadian variations of histamine, epinephrine, and cortisolconcentrations. Asthma attacks at night may also be triggered directlyby sleep apnea. Nocturnal asthma attacks may be fatal, particularlywithin patients also suffering from CHF.

In view of the significant adverse consequences of apnea/hypopnea,nocturnal asthma, or CSR, particularly insofar as patients with CHF areconcerned, it is highly desirable to provide techniques for detectingsuch conditions. Tracking actual patient respiration provides perhapsthe most direct and effective technique for detecting respiratorydisorders. For patients with pacemakers and ICDs, respiration isconventionally tracked based on thoracic impedance as measured viapacing/sensing leads implanted within the heart. Sensing of theintracardiac electrogram (IEGM) of the patient is temporarily suspendedduring each cardiac cycle so as to sense an impedance signal, from whichrespiration patterns are derived. See, for example, U.S. Pat. No.6,449,509 to Park, et al., entitled “Implantable Stimulation DeviceHaving Synchronous Sampling for a Respiration Sensor.”

Although impedance-based techniques are useful, it would be desirable toprovide alternative techniques for tracking respiration, particularlyfor the purposes of detecting episodes of abnormal respiration, whereinrespiration is derived solely from the IEGM signal so as to eliminatethe need to detect or process impedance. Additionally, this eliminatesneed for additional sensors, and the sensing electrodes can be thus usedfor IEGM based breathing pattern detection and hence, the ease ofimplementability in current platforms. One technique for derivingrespiration from an IEGM signal is set forth in U.S. Pat. No. 6,697,672to Andersson, entitled “Implantable Heart Stimulator”, which isincorporated by reference herein. Briefly, Andersson provides atechnique to extract parameters related to patient respiration from ananalysis of intervals between various events detected within aventricular-IEGM (i.e. V-IEGM) signal. For example, cycle-to-cyclevariability is tracked in R-R intervals or in the amplitude of S-Tintervals. In other words, the technique of Andersson exploitsinterval-based morphological features of the V-IEGM to trackrespiration. Although not discussed in the Andersson reference,autonomic variability arising during respiration causes theinterval-based changes in the IEGM. R-waves (also referred to asQRS-complexes) are electrical signals representative of thedepolarization of ventricular muscle tissue. The subsequent electricalrepolarization of the ventricular tissue appears within the IEGM as aT-wave. Electrical depolarization of atrial muscle tissue is manifest asa P-wave. Strictly speaking, P-waves, R-waves and T-waves are featuresof a surface electrocardiogram (EKG or ECG). For convenience, the termsP-wave, R-wave and T-wave are also used herein (and in the literature)to refer to the corresponding internal signal component.

Although the interval-based variability technique of Andersson iseffective, it is desirable to provide additional or alternativeIEGM-based techniques for trending and tracking respiration and fordetecting episodes of abnormal respiration. This general goal wasachieved by the techniques of the parent application, cited above.Briefly, respiration patterns are detected based upon cycle-to-cyclechanges in morphological features associated with individual electricalevents with the IEGM signals. For example, slight changes in the peakamplitudes of QRS-complexes, P-waves or T-waves are tracked to identifycyclical variations representative of patient respiration.Alternatively, the integrals of the morphological features of theindividual events may be calculated for use in tracking respiration.Once respiration patterns have been identified, episodes of abnormalrespiration, such as apnea, hyperpnea, nocturnal asthma, or the like,may be detected and therapy automatically delivered.

Hence, the techniques of the parent application, which are alsodescribed herein below, are not limited to analyzing interval-basedfeatures of a V-IEGM, as with certain predecessor techniques. Instead,the techniques of the parent application examine changes withinindividual features of cardiac cycles over time. In this regard, it hasbeen observed that respiration causes slight variations in the size andshape of individual electrical events of the IEGM signals, such asQRS-complexes, and that those changes are correlated with respiration.This differs from changes in intervals (such as R-R intervals), which,as noted, appear to arise due to autonomic variability. In one specificexample, changes in the integrals of the QRS-complex derived from aV-IEGM channel signal are examined, alone or in combination with,integrals of P-waves derived from an atrial IEGM (A-IEGM) channelsignal. Interval-based parameters, such as variations in A-A, R-R or AVintervals, may be additionally used to aid in tracking respiration butare not required.

The parent application also presented techniques for detecting episodesof abnormal respiration based on respiration patterns, such as episodesof such as apnea, hypopnea, nocturnal asthma, or CSR. The presentapplication is primarily directed to providing further improvements inthe area of abnormal respiration detection.

SUMMARY

In accordance with one illustrative embodiment, techniques are providedfor detecting abnormal respiration within a patient using an implantablemedical device. In one example, IEGM signals are sensed and individualcardiac cycles are identified therein. Selected individual electricalevents (such as P-waves, QRS-complexes or T-waves) are identified withinthe cardiac cycles and one or more morphological or temporal parametersassociated with the individual features are detected (such as maximumamplitude, peak-to-peak amplitude, or numerical integral of thefeature). Patient respiration is detected based on cycle-to-cyclechanges in the detected parameters associated with the individualselected electrical events. Then, abnormal respiration is detected byidentifying individual respiratory cycles within the patientrespiration, detecting one or more parameters associated with theindividual respiratory cycles, detecting any significant changes in theparameters associated with the individual respiratory cycles, and thenevaluating the significant changes to detect abnormal respiration.Exemplary parameters associated with the individual respiratory cyclesinclude the inter-breath interval, respiration depth, standard deviationin respiration depth, and median respiration depth. Respiration power,which may encompass multiple respiratory cycles, is also preferablydetermined.

Hence, in the example, episodes of abnormal respiration—such as apnea,hypopnea, nocturnal asthma, or CSR—are detected based on significantchanges in respiratory parameters, which are in turn derived from themorphological and temporal parameters of cardiac cycles within the IEGM.The techniques are particularly well suited to detecting episodes ofabnormal respiration during sleep. In this regard, normal respirationduring sleep is characterized by an almost constant respiration depth(corrected for patient posture and other non-respiratory factors).Hence, significant changes in respiration depth or other parametersassociated with the respiratory cycles are indicative of a transitionfrom normal respiration to some form of abnormal respiration. Furtheranalysis of the respiratory parameters is used to identify theparticular form of abnormal respiration. The technique can also be usedto track and trend sleep disorder breathing or, in general, disorderedbreathing. Depending upon the capabilities of the implanted device,appropriate therapy may then be delivered. For example, an alarm devicemay be triggered to alert the patient upon detection of an episode ofapnea/hypopnea. The alarm device may be, e.g., an implanted device suchas a “tickle” voltage warning device or a bedside warning system thatemits an audible alarm. In this manner, if the patient is asleep, thepatient is thereby awakened so as to prevent extended episodes ofapnea/hypopnea from occurring, which can cause significant variances inblood chemistry that can exacerbate other medical conditions such asCHF.

In addition, if a determination has been made by the implanted systemthat the patient is subject to frequent episodes of apnea or hypopnea,dynamic atrial overdrive (DAO) pacing may be delivered in an effort toprevent additional episodes from occurring. If an implantable drug pumpis provided, the implanted system may be programmed to selectivelydeliver medications deemed effective in addressing abnormal respirationor effective in addressing the underlying medical condition causing theabnormal respiration (which may be, e.g., CHF). In addition, regardlessof the type of therapy, diagnostic information is preferably recordedwithin a memory of the implanted system for subsequent review by aphysician.

Thus, by analyzing respiratory parameters derived from IEGM signals,abnormal respiration can be detected using a pacemaker or ICD withoutrequiring additional leads or sensors beyond those otherwise employed incardiac sensing/pacing. In the alternative, the detection techniques ofthe invention may be implemented within other implantable devicesbesides pacemakers or ICDs, such as dedicated devices providedspecifically for detecting episodes of abnormal respiration.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further features, advantages and benefits of the inventionwill be apparent upon consideration of the descriptions herein taken inconjunction with the accompanying drawings, in which:

FIG. 1 illustrates pertinent components of an implantable medical systemhaving a pacer/ICD capable of: tracking respiration patterns based onIEGM signals detected via leads mounted in the heart; detecting episodesof abnormal respiration based on the respiration patterns; anddelivering therapy or warning signals in response thereto;

FIG. 2 is a flow chart providing an overview of the method for trackingrespiration patterns based on IEGM signals, which may be performed bythe system of FIG. 1;

FIG. 3 is a flow chart specifically illustrating depolarization-basedrespiration detection techniques, which may be performed by the systemof FIG. 1;

FIG. 4 is a graph illustrating exemplary unipolar IEGM patterns andresulting respiration patterns derived from an analysis of the integralsof QRS complexes generated via the method of FIG. 3;

FIG. 5 is a graph illustrating exemplary unipolar IEGM patterns (shownsuperimposed on one another) and resulting respiration patterns derivedfrom an analysis of the integrals of QRS-complexes generated via themethod of FIG. 3;

FIG. 6 is a graph illustrating respiration patterns derived from ananalysis of the peak-to-peak amplitudes of P-waves generated via themethod of FIG. 3;

FIG. 7 is a graph illustrating respiration patterns derived from ananalysis of the peak-to-peak amplitudes of intrinsic QRS-complexesgenerated via the method of FIG. 3;

FIG. 8 is a flow chart specifically illustrating repolarization-basedrespiration detection techniques, which may be performed by the systemof FIG. 1;

FIG. 9 is a graph illustrating exemplary unipolar ventricle paced IEGMpatterns and resulting respiration patterns derived from an analysis ofthe integrals of T-waves generated via the method of FIG. 8;

FIG. 10 is a graph illustrating exemplary unipolar intrinsic VentricularIEGM patterns (shown superimposed on one another) and resultingrespiration patterns derived from an analysis of the integrals ofT-waves generated via the method of FIG. 8;

FIG. 11 is a graph illustrating respiration patterns derived from ananalysis of the peak-to-peak amplitudes of T-waves generated via themethod of FIG. 8;

FIG. 12 is a graph illustrating respiration patterns derived from ananalysis of the maximum amplitudes of T-waves generated via the methodof FIG. 8;

FIG. 13 is a flow chart specifically illustrating interval-basedrespiration detection techniques, which may be performed by the systemof FIG. 1;

FIG. 14 is a flow chart illustrating abnormal respiration detectiontechniques, which may be performed by the system of FIG. 1, based onrespiration patterns detecting using the techniques of FIGS. 2-13;

FIG. 15 is a graph illustrating a sixteen hour respiration patternderived from an analysis of the maximum amplitudes of T-waves for use inidentifying trends for use with the method of FIG. 14;

FIG. 16 is a simplified, partly cutaway view, illustrating the pacer/ICDof FIG. 1 along with a complete set of leads implanted in the heart of apatient;

FIG. 17 is a functional block diagram of the pacer/ICD of FIG. 16,illustrating basic circuit elements that provide cardioversion,defibrillation and/or pacing stimulation in four chambers of the heartand particularly illustrating an IEGM-based respiration patterndetector, an IEGM-based abnormal respiration episode detector, and anabnormal respiration therapy controller; and

FIG. 18 is a flow chart providing an overview of improved methods fordetecting abnormal respiration patterns, which may be performed infurtherance of the techniques of FIG. 14;

FIG. 19 is a flow chart illustrating exemplary steps directed toidentifying individual respiratory cycles in accordance with thetechnique of FIG. 18;

FIG. 20 is a graph illustrating an exemplary cardiac cycle andparticularly illustrating historical ranges of values for the T-wavethat may used in connection with the techniques of FIG. 19 to extracttemporal and morphological parameters;

FIG. 21 is a graph illustrating an exemplary distribution of paceddepolarization (PDI) values that may be analyzed via the techniques ofFIG. 19 to reject fused beats;

FIG. 22 is a graph illustrating exemplary cardiac cycles and variousparameters derived therefrom via the techniques of FIG. 19, along withexternally-derived signals representative of patient respiration forcomparison;

FIG. 23 is a flow chart illustrating exemplary steps directed toidentifying detecting parameters associated with individual respiratorycycles in accordance with the technique of FIG. 18;

FIG. 24 is a graph illustrating exemplary respiratory cycle parametersthat may be analyzed via the techniques of FIG. 19, along withexternally-derived signals representative of patient respiration forcomparison;

FIG. 25 is a flow chart illustrating exemplary steps directed todetecting significant changes in the respiratory parameters inaccordance with the technique of FIG. 18;

FIG. 26 is a flow chart illustrating exemplary steps directed toevaluating the significant changes to detect abnormal respiration inaccordance with the technique of FIG. 18; and

FIG. 27 is a flow chart summarizing the overall abnormal respirationdetection procedures of FIGS. 18-26.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description includes the best mode presently contemplatedfor practicing the invention. This description is not to be taken in alimiting sense but is made merely to describe general principles of theinvention. The scope of the invention should be ascertained withreference to the issued claims. In the description of the invention thatfollows, like numerals or reference designators are used to refer tolike parts or elements throughout.

Overview of Implantable Medical System

FIG. 1 illustrates an implantable medical system 8 having a pacer/ICDcapable of tracking respiration based on IEGM signals, identifyingepisodes of abnormal respiration and delivering appropriate therapy. Tothis end, pacer/ICD 10 receives voltage signals from various cardiacpacing leads (only two of which are shown in the FIG. 1) from whichvarious channels of IEGM signals are derived including, for example,unipolar or bipolar A-IEGM signals and unipolar or bipolar V-IEGMsignals. A complete set of exemplary pacing leads are shown in FIG. 16from which a wide variety of specific channels of IEGM signals may bederived. Based on the IEGM signals, the pacer/ICD detects parametersassociated with patient respiration using a technique broadly summarizedbelow with reference to FIG. 2. Examples that are more specific are setforth in FIGS. 3-13. Based on the respiration parameters, the pacer/ICDthen also detects individual episodes of abnormal respiration, such asapnea, asthma or CSR.

Once an episode of abnormal respiration has been detected, the pacer/ICDuses additional implanted components (if so equipped) to deliverappropriate therapy or warning signals. For example, if apnea/hypopneais detected, the pacer/ICD may activate an internal alarm 18 or anexternal bedside alarm 22. Internal alarm 18 may be a vibrating deviceor a “tickle” voltage device that, in either case, provides perceptiblestimulation to the patient to alert or awaken the patient so as toterminate the episode of apnea/hypopnea. The bedside alarm may provideaudible or visual alarm signals of sufficient magnitude to alert orawaken the patient. If an activity sensor is provided within thepacer/ICD, the form of the alarm may be controlled based on patientactivity. For example, if the activity level indicates that the patientis asleep, a more noticeable alarm may be employed than if the patientis deemed to be awake. In addition, while the patient is asleep, theintensity of the alarm signal can be periodically increased until thepatient awakens, as detected by the activity sensor. Additionally, or inthe alternative, the system may include a drug pump 20 capable of thedelivering medications in an attempt to prevent the onset of additionalepisodes of apnea/hypopnea. Discussions of exemplary medications areprovided below. In addition, the pacer/ICD may deliver atrial overdrivepacing for the purposes of preventing additional episodes ofapnea/hypopnea from occurring.

Thus, FIG. 1 provides an overview of an implantable system for trackingrespiration, detecting episodes of abnormal respiration and fordelivering therapy in response thereto. Although a pacer/ICD isillustrated in FIG. 1, it should be understood that the detectiontechniques of the invention may be implemented within other implantabledevices, including dedicated respiration detection devices notnecessarily capable of providing cardiac stimulation therapy. Note alsothat internal signal transmission lines for interconnecting the variousimplanted components are not shown. Alternatively, wireless signaltransmission may be employed. In addition, it should be appreciated thatsystems provided in accordance with invention need not include all thecomponents shown in FIG. 1. In many cases, for example, the system willinclude only the pacer/ICD and its leads. Other implementations willemploy internal or external alarms but no drug pumps. These are just afew exemplary embodiments. No attempt is made herein to describe allpossible combinations of components that may be provided in accordancewith the general principles of the invention. Also, note that theparticular locations of the implanted components are merely exemplary.

Overview of Technique for Tracking Respiration Using IEGM

FIG. 2 provides an overview of the techniques of the invention fortracking respiration patterns via morphological features of IEGMsignals. Initially, at step 100, IEGM signals are sensed by animplantable medical device, such as the pacer/ICD of FIG. 1. As will beexplained in greater detail with reference to the specific examplesbelow, the IEGM signals may be atrial channel signals, ventricularchannel signals, cross chamber signals, or some combination thereof. Inany case, at step 102, individual cardiac cycles are identified withinthe IEGM signals using otherwise conventional detection techniques,which typically operate to detect cardiac cycles based on QRS-complexes.At step 104, selected electrical events are identified within the IEGMsignals, such as depolarization events (e.g. QRS-complexes or P-waves)or repolarization events (i.e. T-waves). At step 106, morphologicalparameters associated with the individual events are detected—such asthe maximum amplitude of the event, the peak-to-peak amplitude of theevent, or the numerical integral of the event (i.e. the total energyassociated with the event). As will be explained, multiple parametersmay be detected for each individual event (e.g. both the maximumamplitude and integral of an event may be detected) and differentparameters may be detected for different events (e.g. the peak-to-peakamplitude may be determined for P-waves whereas an integral may becalculated for T-waves.)

Then, at step 108, patient respiration is detected based oncycle-to-cycle changes in the morphological parameters (such ascycle-to-cycle variations in the integral of the QRS-complexes orcycle-to-cycle changes in the maximum amplitudes of P-waves). In otherwords, changes in morphology of a given parameter from one beat toanother are tracked for the purposes of detecting respiration patterns.This differs from changes in intervals (such as R-R intervals), which,as noted above, appear to arise due to autonomic variability.

The slight variations in the morphology of individual events within theIEGM are tracked from cycle-to-cycle so as to detect the cyclicalchanges associated with normal respiration. Otherwise conventionalfilters may be used to isolate cyclical patterns appearing atfrequencies associated with respiration. Additionally, an analysis ofchanges in the intervals between beats may be used to enhance thereliability of the respiration detection technique of step 108. Inparticular, the techniques described in the above-referenced patent toAndersson may be employed. Variability in AV or A-A intervals may alsobe employed. In other words, both interval-based and individualfeature-based techniques may be employed to enhance detectionspecificity.

Depolarization-Based Respiration Detection Examples

Turning now to the FIGS. 3-7, examples of the technique specificallydirected to the use of depolarization-based parameters (i.e. P-waves andQRS-complexes) will now be described. Beginning at steps 200 and 202,the pacer/ICD (or other implanted medical device) senses A-IEGM andV-IEGM channel signals. The signals may be derived from a leadconfiguration that may be unipolar, bipolar or cross-chamber (i.e.signals sensed between electrodes is separate chambers such as betweenA-tip to V-tip.) The specific channel used may be right atrium,right/left Ventricle. Far field QRS or T-waves can be analyzed in theatrial channel i.e. far-field R-waves and ventricular repolarization(T-waves). Additionally, the Ventricular channel near-field QRS andT-waves can be used. In any case, at step 204, selecteddepolarization-based features are detected within the IEGM signals.Examples include P-waves, QRS-complexes, atrial evoked responses (AERs)and ventricular evoked responses (VERs). Different features may bedetected within different IEGM channel signals. For example, P-waves andAERs may be detected within A-IEGM channel signals; whereasQRS-complexes and VERs may be detected within V-IEGM channel signals. Atstep 206, the pacer/ICD calculates the numerical integral, the maximumamplitude and/or the peak-to-peak amplitude of the detected features orother suitable morphological parameters. Maximum or peak amplitude of anevent represents the maximum of the absolute value of the respectiveIEGM channel signal during the event. Peak-to-peak amplitude insteadrepresents the difference between the highest and lowest values of therespective IEGM channel signal during the event. With a baseline voltageof zero, the peak-to-peak amplitude thereby represents the differencebetween the largest positive and largest negative values of the IEGMsignal during the event. Insofar as integrals of paced events areconcerned, a paced depolarization integral (PDI) is preferablycalculated. Otherwise conventional techniques for calculating PDIs maybe employed. See, for example, U.S. Pat. No. 6,731,985 to Poore, et al.,entitled “Implantable Cardiac Stimulation System and Method forAutomatic Capture Verification Calibration.”) For paced beats, the PDIis more useful than QRS amplitude because the PDI provides an improvedthe signal-to-noise ratio. In any case, at step 208, the pacer/ICDtracks changes in the calculated parameters from cycle-to-cycle(typically over at least a few dozen cardiac cycles) and, at step 210,derives respiration parameters from those changes.

Specific examples are illustrated in FIGS. 4-7. Within FIG. 4, a firstgraph 212 illustrates a unipolar V-IEGM signal for a canine test subjectpaced at 90 beats per minute (bpm). The vertical axis of the graphillustrates the output of an analog-to-digital converter (ADC) appliedto the unipolar IEGM signal. The output ADC count is represented onarbitrary numerical scale (scaled to have only positive values), whichis generally representative of the voltage associated with the unipolarIEGM signals, scaled so that all values are positive. FIG. 4 shows theIEGM signals associated with a plurality of cardiac cycles superimposedone upon the other so as to particularly illustrating slight variationsin the shape of the patterns from cycle-to-cycle. The horizontal axisrepresents time within the individual cardiac cycles in millisecondsfrom a common starting point. A second graph, 214, illustrates theintegrals of the VERs associated with each ventricular pacing pulse. Inother words, the VER within each individual cardiac cycle of graph 212was numerically integrated to obtain a single value, then those valueswere plotted as a function of time to produce graph 214. (The integralsof VERs are also referred to as PDIs.) Within graph 214, the verticalaxis represents the integral value in arbitrary numerical units. Thevertical horizontal axis represents time in seconds.

The integrals from the VERs of graph 212 have been connected by aninterpolated line so as to provide a smooth representation of therespiration pattern of the canine test subject. More complex methods maybe used to interpolate by using curve fitting of functions or by using areconstruction filter. When a patient is awake/active, curve fitting caninstead be used to provide smooth representation to calculate thebreathing rate. As can be seen, there is clearly a cyclical patterncomposed of alternating peaks and nadirs, from which respirationinformation may be derived. In particular, the rate of respiration maybe derived (using otherwise conventional signal processing and analysistechniques) based upon the interval from one peak to another or theinterval from one nadir to another. The relative amplitude ofrespiration may be derived based on comparison of the amplitude of therespective peaks and nadir of a given respiration cycle. Hence, althoughthe respiration pattern of graph 214 does not necessarily closelyrepresent an actual canine respiration pattern (which is typically moresinusoidal, particularly during fairly fast-paced respiration), therespiration pattern of graph 214 is nevertheless sufficient to obtaingross information pertaining to respiration, such as rate and relativeamplitude from which episodes of abnormal respiration may be detected.

A second depolarization-based example is shown in FIG. 5, again for acanine test subject (although in this case, using intrinsic beats ratherthan paced beats). A first graph 216 illustrates QRS-complexes (as wellas T-waves) derived from a unipolar ventricular lead. The IEGM signalsassociated with a plurality of cardiac cycles are superimposed one uponthe other. Again, the vertical axis illustrates voltage in terms of anADC count. The horizontal axis represents time within the individualcardiac cycles in milliseconds from a common starting point. The graph218 shows a resulting, smoothed respiration pattern derived byintegrating the QRS complexes shown in graph 216. Again, a cyclicalpattern appears, which is representative of the respiration of thecanine test subject. Gross information pertaining to respiration may bederived, including respiration rate and relative amplitude.

Another depolarization-based example is shown in FIG. 6, this one basedon P-wave maximum (or peak) amplitude. In FIG. 6, only the resultingrespiration pattern is shown (by way of graph 220), not the P-wavesthemselves. The vertical axis represents peak amplitude on an arbitraryvoltage scale; whereas the horizontal axis illustrates time in seconds.The data was derived from a canine test subject based on atrial unipolarIEGM channel signals. The peak amplitude of each P-wave within eachcardiac cycle was detected and the resulting plot of P-wave amplitude asa function of beat number smoothed. As with the other plots, a cyclicalpattern is apparent, which is representative of respiration. A finaldepolarization-based example is shown in FIG. 7, which illustrates arespiration pattern (via graph 222) derived from QRS-complex maximumamplitudes sensed via a unipolar ventricular lead for a canine testsubject. The maximum or peak amplitude of the QRS-complex of eachcardiac cycle was detected, plotted along a time axis, and the resultinggraph smoothed. Again, the gross features of the canine respirationpattern are clearly evident.

Thus, FIGS. 4-7 illustrate various examples of respiration patternsderived by separately plotting various depolarization-based features ofIEGM signals. It is possible to also use two or more separate parametersto track respiration to improve accuracy. For example, respiration plotsmay be generated based upon QRS complexes derived separately fromdifferent channels, with the separate plots then merged to yield asingle respiration plot (using, for example, otherwise conventionalinterpolation techniques). Alternatively, respiration patterns derivedfrom different parameters, such as one from QRS-complexes and one fromP-waves may be merged and combined to form a smooth time domaininterpolation of breathing pattern. Additional morphological parameters,such as the peak slope of an event, might be suitable as well. Otherwiseroutine experimentation may be performed to determine which additionalparameters might be suitable for tracking respiration and to identifythe optimal combinations of various parameters to be combined so as tomost reliably track patient respiration.

Repolarization-Based Respiration Detection Examples

Turning now to the FIGS. 8-12, examples specifically directed to the useof repolarization-based parameters (i.e. T-waves) will now be described.Many of the features of these techniques are similar to thedepolarization-based techniques already described and so only pertinentdifferences will be described. Beginning at step 300 of FIG. 8, thepacer/ICD senses V-IEGM signals (or senses cross-chamber signals havingstrong ventricular components.) At step 302, a repolarization-basedfeature is detected within the IEGM signals, typically the ventricularT-wave. Potentially, atrial repolarization events may also be detectedand used to track respiration. However, atrial repolarization events aretypically of low amplitude and difficult to detect and thus are notoptimal for use in tracking respiration. Hence, the use of atrialrepolarization events will not be described in any great detail herein.)At step 304, the pacer/ICD calculates the numerical integral, themaximum amplitude and/or the peak-to-peak amplitude of the detectedT-waves. At step 306, the pacer/ICD tracks changes in the calculatedT-wave parameters from cycle-to-cycle and, at step 308, derivesrespiration based on changes in the T-waves. In this regard, far-fieldventricular depolarization events as detected in the atrium can be usedto aid in tracking repolarization.

Specific T-wave-based examples are illustrated in FIGS. 9-12 for acanine test subject. Within FIG. 9, graph 312 illustrates a unipolarV-IEGM signal for a canine test subject paced at 90 bpm. FIG. 9 showsthe IEGM signals associated with a plurality of cardiac cyclessuperimposed one upon the other. The horizontal axis represents timewithin the individual cardiac cycles in milliseconds from a commonstarting point. Graph 314 illustrates the integrals of the T-waves fromcycle-to-cycle. The individual data points derived by integrating theT-waves of graph 312 have been interpolated so as to provide a smoothrepresentation of the respiration pattern of the canine test subject. Acyclical pattern appears, from which respiration information may bederived. Although the respiration pattern of graph 314 does not closelyrepresent an actual respiration pattern, graph 314 is neverthelesssufficient to obtain gross information pertaining to patientrespiration, such as rate, depth and effort.

A second repolarization-based example is shown in FIG. 10, again for acanine test subject (although in this case, using intrinsic beats ratherthan paced beats). A first graph 316 illustrates T-waves (as well asQRS-complexes) derived from a unipolar ventricular lead, with theT-waves superimposed one upon the other. Graph 318 shows the resulting,interpolated respiration pattern derived by integrating the T-waves.Again, a cyclical pattern appears, representative of the respiration ofa canine test subject from which gross information pertaining torespiration may be derived, including respiration rate and relativeamplitude.

Another repolarization-based example is shown in FIG. 11, this one basedon T-wave maximum amplitude. In FIG. 11, only the resulting respirationpattern shown (by way of graph 320), not be the T-waves themselves. Thepeak amplitude of each T-wave within each cardiac cycle was detected andthe resulting plot of T-wave amplitude as a function of beat number issmoothed. As with the other plots, a clearly cyclical pattern appears,representative of respiration. A final repolarization-based example isshown in FIG. 12, which illustrates a respiration pattern (via graph322) derived from T-wave maximum amplitudes. The maximum amplitude ofthe T-wave of each cardiac cycle was detected, plotted along a timeaxis, and the resulting graph smoothed. Again, the gross features of therespiration pattern of the canine test subject are clearly evident.

Thus, FIGS. 8-12 illustrate various examples of respiration patternsderived by separately plotting various repolarization-based features ofIEGM signals. It is possible to also use two or more separate T-waveparameters to track respiration, such as both maximum amplitude andpeak-to-peak amplitude. Alternatively, respiration patterns derived fromdifferent events, such as one from QRS-complexes and one from T-wavesmay be merged. Using multiple parameters or multiple IEGM channelsignals may enhance the specificity with which respiration may betracked. Otherwise routine experimentation may be performed to determineoptimal combinations of the parameters to be combined so as to mostreliably track patient respiration.

Interval-Based Respiration Detection Examples

In addition to the aforementioned beat-by-beat tracking techniques,which seek to track respiration based on changes in morphology,intervals between successive beats (or between successive features of anindividual beat) may additionally, or alternatively, be employed. Thisis summarized in FIG. 13. Beginning at steps 400 and 402, the pacer/ICDsenses A-IEGM and V-IEGM signals. At step 404, pacer/ICD then detectssequences of consecutive P-waves and/or R-waves within the IEGM signals.At step 406, selected intervals are calculated, e.g., A-A intervals, AVintervals or R-R intervals. A-A intervals, which represent the intervalsbetween consecutive P-waves, are preferably derived from A-IEGM signals;whereas R-R intervals, which represent the intervals between consecutiveQRS-complexes, are preferably derived from V-IEGM signals. Typically, apacer/ICD calculates these intervals as part of its routine operations.In any case, at step 408, the pacer/ICD then tracks changes in theintervals from cycle-to-cycle and, at step 410, derives respiration fromchanges in the intervals over time, typically, over at least a few dozencardiac cycles. For example, a given interval value, such as an A-Ainterval derived from an A-IEGM signals, may be calculated between eachsuccessive pair of P-waves, with the value of the interval then plottedas a function of time or as a function of cardiac cycle number (as insome of the examples described above) so as to track the cyclicalfeatures of respiration. As before, the respiration patterns derived inthis manner may not closely approximate the actual smooth respirationpatterns of patient, but nevertheless provide sufficient informationfrom which respiration rate and relative amplitude can be derived andfrom which episodes of abnormal respiration may be detected.

The use of changes in R-R and ST intervals derived from V-IEGM signalsare described in detail with reference to the Andersson cited above. Thetechnique of FIG. 13, however, is not limited to R-R/ST intervalvariability derived from V-IEGM signals but, as noted, may be based oneither A-IEGM or V-IEGM signals, and may further be based on A-Aintervals or AV intervals, or some combination thereof. Unipolar orBipolar signals may be employed, as well as cross-chamber signals. Therespiration patterns derived from cycle-to-cycle changes in intervalsmay be merged with respiration patterns derived from depolarizationevents or repolarization events, described above, to provide furtherspecificity.

Abnormal Respiration Detection and Therapy

What have been described thus far are various techniques for trackingrespiration patterns based on features of IEGM signals. With referenceto FIG. 14, techniques for detecting episodes of abnormal respirationbased on the respiration patterns and delivering therapy will now bedescribed. At step 450, the pacer/ICD analyzes the detected respirationpattern to detect episodes of abnormal respiration such as apnea andhypopnea, hyperpnea, nocturnal asthma, and CSR, based on respirationrate and/or amplitude. In general, otherwise conventional techniques fordetecting episodes of abnormal respiration, which use respiration ratesor respiration amplitudes, may be employed. Preferably, however, thetechniques set forth in FIGS. 18-27 are employed to detect abnormalrespiration. These techniques are described below. Once an episode ofabnormal respiration is detected, otherwise conventional techniques maybe used to identify the particular form of abnormal respiration, i.e. todistinguish between apnea, hypopnea, hyperpnea, CSR, etc.

Briefly, apnea may be detected based upon a lack of any significantamplitude variations within the detected respiration patterns extendingover a predetermined period of time. In one example, an apnea detectionamplitude threshold value may be specified along with an apnea detectiontime threshold value. If the respiratory amplitude derived from therespiration patterns does not exceed the amplitude threshold value forat least a period of time greater than the time threshold value, thenapnea is presumed. Typically, an episode of apnea is not deemed to haveoccurred unless there is a lack respiration for at least ten seconds andso a time threshold of at least ten seconds may be employed. A suitableamplitude threshold value may be determined via routine experimentationfor use with respiration patterns derived from particular IEGMparameters. In this regard, the amplitude threshold value for use withthe respiration patterns derived from QRS-complexes may differ from onederived from the P-waves or T-waves. The values may also differ frompatient to patient. Note that the amplitude and morphology changes mayalso depend on body position, and also on the rhythm types, i.e. in caseof a pacer/ICD, it would be advisable to have different thresholds fordifferent rhythm combinations: A-R, A-V, P-R, P-V. Suitable amplitudethreshold values may be specified following implant of device based onthe specific characteristics of patient in which the device is implantedor automatically updated during routine working of the algorithm.

More complex techniques may be employed identifying each episode ofapnea. In one example, a combination of raw respiration parametersgenerated above and various thresholds on each parameter are fed into anapnea episode detection system, specifically configured for detectingapnea. Additionally, simple or more complex methods than zero crossingscan be used to determine breathing rate. Depth of breathing and effortof breathing can be calculated. The local variability in each parameteras derived from mean and stand-deviations etc. may also be fed asvariables into the apnea episode detection system. (Other variables thatcan be derived include median, peak-to-peak changes, and inter-quartilerange.) Using the long term autonomic interval-based variability as wellas the individual event-based morphological variability, it is possibleto detect differences between obstructive apnea, central apnea,nocturnal apnea, CSA and flow hypopnea etc.

Hypopnea may be detected based upon respiratory amplitude that exceedsthe apnea threshold but falls below a separate hypopnea amplitudethreshold. As with apnea, a time threshold value (such as 10 seconds)may be specified as well. Hence, if there is at least some respiration,but the amplitude of that respiration falls below an amount deemedhealthy for the patient, hypopnea is presumed. As with the various apneathresholds, separate hypopnea threshold values may be specified for usewith different respiration detection techniques (i.e.depolarization-based techniques versus repolarization-based techniques)and for use with different patients, preferably determined on a patientby patient basis following implant of the device. Alternative and morecomplex hypopnea detection techniques may be employed as well.

Hyperpnea/asthma may be detected based upon a pattern exhibitingexcessively rapid respiration (or attempted respiration.) Accordingly,an hyperpnea/asthma amplitude detection threshold may be specified alongwith a hyperpnea/asthma respiration rate and effort threshold. Ifamplitude derived from the respiration pattern exceeds thehyperpnea/asthma respiration amplitude detection threshold while therespiration rate (also derived from the respiration pattern) alsoexceeds it respective threshold, hyperpnea/asthma is thereby presumed.Again, suitable thresholds may be determined on the patient basisfollowing implant of device. Alternative and more complexhyperpnea/asthma or CSR detection techniques may be employed as well.

Hyperpnea usually may be distinguished from asthma based on the presenceor absence of normal respiration preceding the attack. Hyperpnea usuallyfollows an episode of apnea/hypopnea; whereas asthma usually follows aperiod of otherwise normal breathing. Episodes of nocturnal asthma maybe distinguished from other asthma attacks merely by determining whetherthe patient is asleep, using otherwise conventional sleep detectiontechniques. Examples of sleep detection techniques are set forth in:U.S. Pat. No. 5,476,483, to Bornzin et al., entitled “System and Methodfor Modulating the Base Rate During Sleep for a Rate-responsive CardiacPacemaker” and U.S. Pat. No. 6,128,534 to Park et al., entitled“Implantable Cardiac Stimulation Device And Method For Varying PacingParameters To Mimic Circadian Cycles.”

CSR may be detected using otherwise conventional techniques based on itscharacteristic pattern of alternating periods of apnea/hypopnea andhyperpnea. See, e.g., U.S. Pat. No. 6,830,548 to Bonnet, et al., “ActiveMedical Device Able to Diagnose a Patient Respiratory Profile.”

Once an episode of abnormal respiration has been detected then, at step452, the pacer/ICD delivers appropriate therapy (assuming it is properlyequipped). For example, in response to detection of frequent episodes ofapnea/hypopnea, atrial overdrive pacing therapy may be applied in anattempt to prevent the onset of additional episodes. A particularlyeffective atrial overdrive pacing technique, referred to herein asdynamic atrial overdrive (DAO) pacing, is described in U.S. Pat. No.6,519,493 to Florio et al., entitled “Methods and Apparatus forOverdrive Pacing Heart Tissue Using an Implantable Cardiac StimulationDevice”. Routine experimentation may be performed to identify optimalDAO pacing parameters for use with patients with apnea/hypopnea. Theaggressiveness of DAO therapy may be adjusted based upon the frequencyor duration of episodes of apnea/hypopnea.

Anti-apneic medications may be delivered via an implantable drug pump,if so equipped. Examples of medications that may be helpful in patientswith apnea are set forth the following U.S. Pat. Nos. 6,331,536 toRadulovacki, et al., entitled “Pharmacological Treatment for SleepApnea”; 6,432,956 to Dement, et al., entitled “Method for Treatment ofSleep Apneas”; 6,586,478 to Ackman, et al., entitled “Methods andCompositions for Improving Sleep”; and 6,525,073 to Mendel, et al.,entitled “Prevention or Treatment of Insomnia with a Neurokinin-1Receptor Antagonist”. Depending upon the particular medication,alternative compounds may be required for use in connection with animplantable drug pump. Routine experimentation may be employed toidentify medications for treatment of sleep apnea that are safe andeffective for use in connection with an implantable drug pump. Dosagesmay be titrated based upon the frequency or duration of episodes ofapnea.

During the actual episode of apnea/hypopnea, an implantable alarm (suchas alarm 18 of FIG. 1) may be activated to awaken the patient (assumingthe patient is sleeping) in an attempt to terminate the episode ofapnea/hypopnea. Alternatively, a bedside alarm may be activated bytransmission of appropriate wireless control signals. Activation of analarm to awaken the patient is preferably employed only if other therapyis found to be ineffective, since awakening the patient interrupts withthe patient's natural sleeping patterns.

If implantable phrenic nerve stimulators are implanted, apnea/hypopneatherapy can also involve delivery of rhythmic electrical stimulation tothe phrenic nerves to mimic breathing (assuming the apnea/hypopnea isdue to a lack of phrenic nerve signals.) Examples of phrenic nervestimulators are set forth in U.S. Pat. No. 5,056,519 to Vince, entitled“Unilateral Diaphragmatic Pacer” and in U.S. Pat. No. 6,415,183 toScheiner, et al., entitled “Method and Apparatus for DiaphragmaticPacing”, which are incorporated by reference herein. Other respiratorynerves may be stimulated as well. U.S. Pat. No. 5,911,218 to DiMarco,entitled “Method and Apparatus for Electrical Stimulation of theRespiratory Muscles to Achieve Artificial Ventilation in a Patient”describes stimulation of nerves leading to intercostal muscles.

If an implantable hypoglossyl nerve stimulator is implanted, therapy canalso involve delivery of stimulation to the hypoglossyl nerves inresponse to obstructive sleep apnea. Examples of hypoglossyl nervestimulators are set forth in U.S. Patent Application 2003/0216789 ofDeem et al., entitled “Method and System for Treating Sleep Apnea.”

Insofar as CSR therapy is concerned, CSR often arises due to CHF and soCSR can often be remedied by addressing the underlying CHF. See, e.g.U.S. patent application Ser. No. 10/792,305, filed Mar. 2, 2004,entitled “System And Method For Diagnosing And Tracking Congestive HeartFailure Based On The Periodicity Of Cheyne-Stokes Respiration Using AnImplantable Medical Device” (A04P1019). Accordingly, upon detection ofepisodes CSR, the pacer/ICD preferably employs otherwise conventionaltechniques to detect CHF and, if CHF is present, any of a variety oftherapies directed to mitigating CHF may be implemented by the device.For example, cardiac resynchronization therapy (CRT) may be performed toimprove cardiac function. CRT and related therapies are discussed in,for example, U.S. Pat. No. 6,643,546 to Mathis, et al., entitled“Multi-Electrode Apparatus And Method For Treatment Of Congestive HeartFailure”; U.S. Pat. No. 6,628,988 to Kramer, et al., entitled “ApparatusAnd Method For Reversal Of Myocardial Remodeling With ElectricalStimulation”; and U.S. Pat. No. 6,512,952 to Stahmann, et al., entitled“Method And Apparatus For Maintaining Synchronized Pacing”. CHF therapymay also include delivery of medications via an implantable drug pump,if so equipped. Exemplary CHF medications include ACE inhibitors,diuretics, digitalis and compounds such as captopril, enalapril,lisinopril and quinapril. Depending upon the particular medication,alternative compounds may be required for use in connection with animplantable drug pump. Routine experimentation may be employed toidentify medications for treatment of CHF that are safe and effectivefor use in connection with an implantable drug pump.

Additionally, during an individual episode of CSR, the implantable alarmor external bedside alarm may be triggered to awaken the patient tobreak the cycle of CSR. Again, activation of an alarm to awaken thepatient is preferably employed only if other forms of therapy are foundto be ineffective. See, also, U.S. patent application Ser. No.10/844,023, filed May 11, 2004, entitled “System and Method forProviding Demand-Based Cheyne-Stokes Respiration Therapy Using anImplantable Medical Device” (A04P1042).

Insofar as hyperpnea is concerned, hyperpnea may arise during CSR or mayarise during an asthma attack. Hyperpnea arising due to CSR ispreferably addressed via CSR therapy. See, also, U.S. patent applicationSer. No. 10/829,719, filed Apr. 21, 2004, entitled “System and Methodfor Applying Therapy during Hyperpnea Phase of Periodic Breathing Usingan Implantable Medical Device” (A04P1037). Hyperpnea arising due toasthma may be addressed by addressing the asthma via suitablemedications delivered via the implantable drug pump. Examples of asthmamedications are set forth, for example, in U.S. Pat. No. 4,089,959 toDiamond, entitled “Long-Acting Xanthine Bronchodilators and AntiallergyAgents”. Depending upon the particular medication, alternative compoundsmay be required for use in connection with an implantable drug pump.Routine experimentation may be employed to identify medications fortreatment of asthma that are safe and effective for use in connectionwith an implantable drug pump. Dosages may be titrated as needed basedon tracking and trending of such breathing patterns.

Additional techniques may be used, if desired, to corroborate thedetection of an episode of abnormal respiration made using thetechniques of the invention before therapy is delivered. See, e.g., U.S.patent application Ser. No. 10/883,857, filed Jun. 30, 2004, entitled“System And Method For Real-Time Apnea/Hypopnea Detection Using AnImplantable Medical System (A04P1057) and U.S. patent application Ser.No. 10/821,241, filed Apr. 7, 2004, entitled “System And Method ForApnea Detection Using Blood Pressure Detected via an Implantable MedicalSystem” (A04P1034).

Continuing with FIG. 14, at step 454, suitable warning signals may bedelivered to alert the patient, his/her physician or other medicalpersonnel to any episodes of abnormal breathing.

At step 456, the device also tracks and trends changes in respirationpatterns. This may be achieved by continuously monitoring the patientfor apneic or hypopneic events and quantifying the amount of apnea basedon an index. A commonly used index is the apnea hypopnea index (AHI).This index is based on counting apnea and hypopneas that occur over theentire night and dividing the number of apnea and hypopneas by the totalsleep time in hours. This invention provides a surrogate for AHI usingthe number of IEGM detected apneas and hypopneas divided by the totalrest time in hours. The total rest time approximates the total sleeptime and is derived measuring the time that a patient is at profoundrest by using an activity sensor as set forth in: in aforementionedpatent to Bornzin et al. (U.S. Pat. No. 5,476,483.) Alternatively, thetotal sleep time may be estimated using IEGM based respiratory ratetrends. During sleep, the respiration rate diminishes below the wakefulrespiration rates and sleep time may be easily determined using the IEGMbased breathing rate trend. In fact, by making a histogram of the IEGMbreathing rates sampled at equal intervals throughout the day andcounting the number of intervals in the lowest mode an estimate of theduration of sleep may be performed. Another method that may be used toestimate the total sleep time throughout the day depends on a directcurrent (DC) accelerometer to quantify the amount of time that a patientis lying down as set forth in U.S. Pat. No. 6,466,821, to Pianca et al.,entitled “AC/DC Multi-Axis Accelerometer for Determining PatientActivity and Body Position”. It is also be possible to use Heart ratedynamics to differentiate between awake and sleep state. See, Redmond etal., “Cardiorespiratory-based sleep staging in subjects with obstructivesleep apnea,” IEEE Trans Biomed Eng 2005; 53(3):485-96.

An exemplary trend pattern is illustrated in FIG. 15, which provides anexemplary breathing pattern (in terms of breathes per minute) trackedover a period of about sixteen hours. A portion 458 illustraterespiration rate during sleep. By analyzing trends contained inbreathing patterns such as that of FIG. 15, the aforementioned surrogateAHI value can be obtained.

Returning to FIG. 14, appropriate diagnostic information is stored atstep 460 so that a medical professional can subsequently review thetrend data or any therapy delivered and evaluate its effectiveness.

What have been described are various techniques for tracking respirationvia IEGM signals, detecting episodes of abnormal respiration anddelivering appropriate therapy. For the sake of completeness, a detaileddescription of an exemplary pacer/ICD for controlling these functionswill now be provided. However, principles of invention may beimplemented within other pacer/ICD implementations or within otherdevices. In particular, techniques of the invention are also applicableto detecting respiration via surface EKG signals and hence are notnecessarily limited to use with implantable devices. In this regard, itis known that the mean cardiac axis and EKG morphology is influenced byelectrode motion relative to the heart and by changes in thoracicelectrical impedance as the lungs fill and empty. The sinus rate ismodulated by vagal influences in synchronization with respiration.Pressure changes (i.e. breathing-related as well as cardiaccycle-related pressure changes) influence IEGM morphology.

Pacemaker/ICD

FIG. 16 provides a simplified block diagram of the pacer/ICD, which is amulti-chamber stimulation device capable of treating both fast and slowarrhythmias with stimulation therapy, including cardioversion,defibrillation, and pacing stimulation (as well as capable of trackingrespiration, detecting episodes of abnormal respiration and deliveringappropriate therapy.) To provide atrial chamber pacing stimulation andsensing, pacer/ICD 10 is shown in electrical communication with a heart612 by way of a left atrial lead 620 having an atrial tip electrode 622and an atrial ring electrode 623 implanted in the atrial appendage.Pacer/ICD 10 is also in electrical communication with the heart by wayof a right ventricular lead 630 having, in this embodiment, aventricular tip electrode 632, a right ventricular ring electrode 634, aright ventricular (RV) coil electrode 636, and a superior vena cava(SVC) coil electrode 638. Typically, the right ventricular lead 630 istransvenously inserted into the heart so as to place the RV coilelectrode 636 in the right ventricular apex, and the SVC coil electrode638 in the superior vena cava. Accordingly, the right ventricular leadis capable of receiving cardiac signals, and delivering stimulation inthe form of pacing and shock therapy to the right ventricle.

To sense left atrial and ventricular cardiac signals and to provide leftchamber pacing therapy, pacer/ICD 10 is coupled to a “coronary sinus”lead 624 designed for placement in the “coronary sinus region” via thecoronary sinus os for positioning a distal electrode adjacent to theleft ventricle and/or additional electrode(s) adjacent to the leftatrium. As used herein, the phrase “coronary sinus region” refers to thevasculature of the left ventricle, including any portion of the coronarysinus, great cardiac vein, left marginal vein, left posteriorventricular vein, middle cardiac vein, and/or small cardiac vein or anyother cardiac vein accessible by the coronary sinus. Accordingly, anexemplary coronary sinus lead 624 is designed to receive atrial andventricular cardiac signals and to deliver left ventricular pacingtherapy using at least a left ventricular tip electrode 626, left atrialpacing therapy using at least a left atrial ring electrode 627, andshocking therapy using at least a left atrial coil electrode 628. Withthis configuration, biventricular pacing can be performed. Although onlythree leads are shown in FIG. 16, it should also be understood thatadditional stimulation leads (with one or more pacing, sensing and/orshocking electrodes) may be used in order to efficiently and effectivelyprovide pacing stimulation to the left side of the heart or atrialcardioversion and/or defibrillation. The leads are also employed forsensing ID tag signals from medications equipped with active IDtransmitters.

A simplified block diagram of internal components of pacer/ICD 10 isshown in FIG. 16. While a particular pacer/ICD is shown, this is forillustration purposes only, and one of skill in the art could readilyduplicate, eliminate or disable the appropriate circuitry in any desiredcombination to provide a device capable of treating the appropriatechamber(s) with cardioversion, defibrillation and pacing stimulation aswell as providing for the aforementioned apnea detection and therapy.The housing 640 for pacer/ICD 10, shown schematically in FIG. 17, isoften referred to as the “can”, “case” or “case electrode” and may beprogrammably selected to act as the return electrode for all “unipolar”modes. The housing 640 may further be used as a return electrode aloneor in combination with one or more of the coil electrodes, 628, 636 and638, for shocking purposes. The housing 640 further includes a connector(not shown) having a plurality of terminals, 642, 643, 644, 646, 648,652, 654, 656 and 658 (shown schematically and, for convenience, thenames of the electrodes to which they are connected are shown next tothe terminals). As such, to achieve right atrial sensing and pacing, theconnector includes at least a right atrial tip terminal (A_(R) TIP) 642adapted for connection to the atrial tip electrode 622 and a rightatrial ring (A_(R) RING) electrode 643 adapted for connection to rightatrial ring electrode 643. To achieve left chamber sensing, pacing andshocking, the connector includes at least a left ventricular tipterminal (V_(L) TIP) 644, a left atrial ring terminal (A_(L) RING) 646,and a left atrial shocking terminal (A_(L) COIL) 648, which are adaptedfor connection to the left ventricular ring electrode 626, the leftatrial tip electrode 627, and the left atrial coil electrode 628,respectively. To support right chamber sensing, pacing and shocking, theconnector further includes a right ventricular tip terminal (V_(R) TIP)652, a right ventricular ring terminal (V_(R) RING) 654, a rightventricular shocking terminal (R_(V) COIL) 656, and an SVC shockingterminal (SVC COIL) 658, which are adapted for connection to the rightventricular tip electrode 632, right ventricular ring electrode 634, theRV coil electrode 636, and the SVC coil electrode 638, respectively.Separate terminals (not shown) may be provided for connecting theimplanted warning/reminder device 18 and the implanted drug pump 20,which are instead shown coupled directly to internal functionalcomponents of the pacer/ICD that control these devices.

At the core of pacer/ICD 10 is a programmable microcontroller 660, whichcontrols the various modes of stimulation therapy. As is well known inthe art, the microcontroller 660 (also referred to herein as a controlunit) typically includes a microprocessor, or equivalent controlcircuitry, designed specifically for controlling the delivery ofstimulation therapy and may further include RAM or ROM memory, logic andtiming circuitry, state machine circuitry, and I/O circuitry. Typically,the microcontroller 660 includes the ability to process or monitor inputsignals (data) as controlled by a program code stored in a designatedblock of memory. The details of the design and operation of themicrocontroller 660 are not critical to the invention. Rather, anysuitable microcontroller 660 may be used that carries out the functionsdescribed herein. The use of microprocessor-based control circuits forperforming timing and data analysis functions are well known in the art.

As shown in FIG. 17, an atrial pulse generator 670 and aventricular/impedance pulse generator 672 generate pacing stimulationpulses for delivery by the right atrial lead 620, the right ventricularlead 630, and/or the coronary sinus lead 624 via an electrodeconfiguration switch 674. It is understood that in order to providestimulation therapy in each of the four chambers of the heart, theatrial and ventricular pulse generators, 670 and 672, may includededicated, independent pulse generators, multiplexed pulse generators orshared pulse generators. The pulse generators, 670 and 672, arecontrolled by the microcontroller 660 via appropriate control signals,676 and 678, respectively, to trigger or inhibit the stimulation pulses.

The microcontroller 660 further includes timing control circuitry (notseparately shown) used to control the timing of such stimulation pulses(e.g., pacing rate, atrio-ventricular (AV) delay, atrial interconduction(A-A) delay, or ventricular interconduction (V-V) delay, etc.) as wellas to keep track of the timing of refractory periods, blankingintervals, noise detection windows, evoked response windows, alertintervals, marker channel timing, etc., which is well known in the art.Switch 674 includes a plurality of switches for connecting the desiredelectrodes to the appropriate I/O circuits, thereby providing completeelectrode programmability. Accordingly, the switch 674, in response to acontrol signal 680 from the microcontroller 660, determines the polarityof the stimulation pulses (e.g., unipolar, bipolar, combipolar, etc.) byselectively closing the appropriate combination of switches (not shown)as is known in the art.

Atrial sensing circuits 682 and ventricular sensing circuits 684 mayalso be selectively coupled to the right atrial lead 620, coronary sinuslead 624, and the right ventricular lead 630, through the switch 674 fordetecting the presence of cardiac activity in each of the four chambersof the heart. Accordingly, the atrial (ATR. SENSE) and ventricular (VTR.SENSE) sensing circuits, 682 and 684, may include dedicated senseamplifiers, multiplexed amplifiers or shared amplifiers. The switch 674determines the “sensing polarity” of the cardiac signal by selectivelyclosing the appropriate switches, as is also known in the art. In thisway, the clinician may program the sensing polarity independent of thestimulation polarity. Each sensing circuit, 682 and 684, preferablyemploys one or more low power, precision amplifiers with programmablegain and/or automatic gain control, bandpass filtering, and a thresholddetection circuit, as known in the art, to selectively sense the cardiacsignal of interest. The automatic gain control enables pacer/ICD 10 todeal effectively with the difficult problem of sensing the low amplitudesignal characteristics of atrial or ventricular fibrillation. Theoutputs of the atrial and ventricular sensing circuits, 682 and 684, areconnected to the microcontroller 660 which, in turn, are able to triggeror inhibit the atrial and ventricular pulse generators, 670 and 672,respectively, in a demand fashion in response to the absence or presenceof cardiac activity in the appropriate chambers of the heart.

For arrhythmia detection, pacer/ICD 10 utilizes the atrial andventricular sensing circuits, 682 and 684, to sense cardiac signals todetermine whether a rhythm is physiologic or pathologic. As used herein“sensing” is reserved for the noting of an electrical signal, and“detection” is the processing of these sensed signals and noting thepresence of an arrhythmia. The timing intervals between sensed events(e.g., P-waves, R-waves, and depolarization signals associated withfibrillation which are sometimes referred to as “F-waves” or“Fib-waves”) are then classified by the microcontroller 660 by comparingthem to a predefined rate zone limit (i.e., bradycardia, normal, atrialtachycardia, atrial fibrillation, low rate VT, high rate VT, andfibrillation rate zones) and various other characteristics (e.g., suddenonset, stability, physiologic sensors, and morphology, etc.) in order todetermine the type of remedial therapy that is needed (e.g., bradycardiapacing, antitachycardia pacing, cardioversion shocks or defibrillationshocks).

Cardiac signals are also applied to the inputs of an analog-to-digital(A/D) data acquisition system 690. The data acquisition system 690 isconfigured to acquire intracardiac electrogram signals, convert the rawanalog data into a digital signal, and store the digital signals forlater processing and/or telemetric transmission to an external device702. The data acquisition system 690 is coupled to the right atrial lead620, the coronary sinus lead 624, and the right ventricular lead 630through the switch 674 to sample cardiac signals across any pair ofdesired electrodes. The microcontroller 660 is further coupled to amemory 694 by a suitable data/address bus 696, wherein the programmableoperating parameters used by the microcontroller 660 are stored andmodified, as required, in order to customize the operation of pacer/ICD10 to suit the needs of a particular patient. Such operating parametersdefine, for example, pacing pulse amplitude or magnitude, pulseduration, electrode polarity, rate, sensitivity, automatic features,arrhythmia detection criteria, and the amplitude, waveshape and vectorof each shocking pulse to be delivered to the patient's heart withineach respective tier of therapy. Other pacing parameters include baserate, rest rate and circadian base rate.

Advantageously, the operating parameters of the implantable pacer/ICD 10may be non-invasively programmed into the memory 694 through a telemetrycircuit 700 in telemetric communication with the external device 702,such as a programmer, transtelephonic transceiver or a diagnostic systemanalyzer. The telemetry circuit 700 is activated by the microcontrollerby a control signal 706. The telemetry circuit 700 advantageously allowsintracardiac electrograms and status information relating to theoperation of pacer/ICD 10 (as contained in the microcontroller 660 ormemory 694) to be sent to the external device 702 through an establishedcommunication link 704. Pacer/ICD 10 further includes an accelerometeror other physiologic sensor 708, commonly referred to as a“rate-responsive” sensor because it is typically used to adjust pacingstimulation rate according to the exercise state of the patient.However, the physiological sensor 708 may, depending upon itscapabilities, further be used to detect changes in cardiac output,changes in the physiological condition of the heart, or diurnal changesin activity (e.g., detecting sleep and wake states) and to detectarousal from sleep. Accordingly, the microcontroller 660 responds byadjusting the various pacing parameters (such as rate, AV Delay, V-VDelay, etc.) at which the atrial and ventricular pulse generators, 670and 672, generate stimulation pulses. While shown as being includedwithin pacer/ICD 10, it is to be understood that the sensor 708 may alsobe external to pacer/ICD 10, yet still be implanted within or carried bythe patient. A common type of rate responsive sensor is an activitysensor incorporating an accelerometer or a piezoelectric crystal, whichis mounted within the housing 640 of pacer/ICD 10. Other types ofphysiologic sensors are also known, for example, sensors that sense theoxygen content of blood, respiration rate and/or minute ventilation, pHof blood, ventricular gradient, etc.

The pacer/ICD additionally includes a battery 710, which providesoperating power to all of the circuits shown in FIG. 17. The battery 710may vary depending on the capabilities of pacer/ICD 10. If the systemonly provides low voltage therapy, a lithium iodine or lithium copperfluoride cell may be utilized. For pacer/ICD 10, which employs shockingtherapy, the battery 710 should be capable of operating at low currentdrains for long periods, and then be capable of providing high-currentpulses (for capacitor charging) when the patient requires a shock pulse.The battery 710 should also have a predictable discharge characteristicso that elective replacement time can be detected. Accordingly,pacer/ICD 10 is preferably capable of high voltage therapy and batteriesor other power sources appropriate for that purpose are employed.

As further shown in FIG. 17, pacer/ICD 10 is shown as having animpedance measuring circuit 712 which is enabled by the microcontroller660 via a control signal 714. Other uses for an impedance measuringcircuit include, but are not limited to, lead impedance surveillanceduring the acute and chronic phases for proper lead positioning ordislodgement; detecting operable electrodes and automatically switchingto an operable pair if dislodgement occurs; measuring respiration orminute ventilation; measuring thoracic impedance for determining shockthresholds; detecting when the device has been implanted; measuringstroke volume; and detecting the opening of heart valves, etc. Theimpedance measuring circuit 120 is advantageously coupled to the switch74 so that any desired electrode may be used.

In the case where pacer/ICD 10 is intended to operate as an implantablecardioverter/defibrillator (ICD) device, it detects the occurrence of anarrhythmia, and automatically applies an appropriate electrical shocktherapy to the heart aimed at terminating the detected arrhythmia. Tothis end, the microcontroller 660 further controls a shocking circuit716 by way of a control signal 718. The shocking circuit 716 generatesshocking pulses of low (up to 0.5 joules), moderate (0.5-10 joules) orhigh energy (11 to 40 joules), as controlled by the microcontroller 660.Such shocking pulses are applied to the heart of the patient through atleast two shocking electrodes, and as shown in this embodiment, selectedfrom the left atrial coil electrode 628, the RV coil electrode 636,and/or the SVC coil electrode 638. The housing 640 may act as an activeelectrode in combination with the RV electrode 636, or as part of asplit electrical vector using the SVC coil electrode 638 or the leftatrial coil electrode 628 (i.e., using the RV electrode as a commonelectrode). Cardioversion shocks are generally considered to be of lowto moderate energy level (so as to minimize pain felt by the patient),and/or synchronized with an R-wave and/or pertaining to the treatment oftachycardia. Defibrillation shocks are generally of moderate to highenergy level (i.e., corresponding to thresholds in the range of 5-40joules), delivered asynchronously (since R-waves may be toodisorganized), and pertaining exclusively to the treatment offibrillation. Accordingly, the microcontroller 660 is capable ofcontrolling the synchronous or asynchronous delivery of the shockingpulses.

Microcontroller 60 also includes an IEGM individual featuremorphology-based respiration detector 701 for detecting respirationbased upon one or more IEGM channel signals using the techniquesdescribed above. An abnormal respiration pattern detector 703 is alsoprovided the purposes of detecting apnea, hypopnea, etc. usingtechniques described above. Additionally, an abnormal respirationtherapy controller 705 is provided for controlling therapy in responseto an episode of abnormal respiration, again using techniques alreadydescribed. Depending upon the implementation, the various components maybe implemented as separate software modules. However, the modules may becombined so as to permit single modules to perform multiple functions.

Further Abnormal Respiration Detection Techniques

Turning now to FIGS. 18-27, techniques particularly directed todetecting abnormal respiration will be described. The techniques may beused, for example, by the pacer/ICD of FIGS. 16-17 to implemented step450 of the technique of FIG. 14.

An overview of these abnormal respiration detection techniques is setforth in FIG. 18. Initially, at step 800, the pacer/ICD identifiesindividual respiratory cycles within patient respiration, i.e. thepacer/ICD identifies individual breaths. Specific techniques foridentifying individual respiratory cycles are discussed below withrespect to FIGS. 19-22. At step 802, the pacer/ICD detects parametersassociated with the individual respiratory cycles such as theinter-breath interval, respiration depth, standard deviation ofrespiration depth, median respiration depth, and respiration power.Specific techniques for detecting such parameters are discussed belowwith respect to FIGS. 23-24. Next, at step 804, the pacer/ICD detectsany significant changes in the parameters associated with the individualrespiratory cycles, i.e. the pacer/ICD detects any significant increaseor decrease in the parameters. Depending upon the particular respiratoryparameters and depending up on the particular form of abnormalrespiration, the changes in the respiratory parameters may be abrupt orgradual. Specific techniques for detecting significant changes inrespiratory parameters are discussed below with respect to FIG. 25.Finally, at step 806, the pacer/ICD, evaluates the significant changesto detect abnormal respiration. In this regard, normal respirationduring sleep is characterized by little or no change in respiratoryparameters such as respiration depth (when corrected for patient postureand other non-respiratory factors). Hence, significant changes inrespiratory parameters such as respiration depth are indicative of, orassociated with, a transition from normal respiration to some form ofabnormal respiration. Specific threshold-based techniques for evaluatingthe changes to detect abnormal respiration are discussed below withrespect to FIG. 25. Once abnormal respiration is detected, varioustechniques may be used to identify the particular form of abnormalrespiration, i.e. to distinguish among apnea, hypopnea, hyperpnea, CSR,etc. Exemplary techniques for distinguishing among different forms ofabnormal respiration are discussed above in connection with FIG. 14.

Turning now to FIGS. 19-20, exemplary techniques for identifyingindividual respiratory cycles will be described for use at step 800 ofFIG. 18. At step 808, the pacer/ICD inputs temporal and morphologicalparameters derived from the IEGM. This may include, e.g., the maximumamplitude, peak-to-peak amplitude, width and integral of individualfeatures of the IEGM such as the intrinsic atrial depolarization events(P-waves), atrial evoked responses (AER), intrinsic ventriculardepolarization events (QRS-complexes), ventricular evoked responses(VER) and premature ventricular events (PVEs). Other exemplaryparameters include: the integral of the ventricular post depolarizationsignal (vPDI), the integral of the atrial post depolarization signal(aPDI), the repolarization integral (tI), a PDI derived from theventricular far-field post depolarization signal (ffPDI), an integral ofthe repolarization far-field signal (fftI). Depending upon the signal,it may be appropriate to separately track near-field and far-fieldsignals (particularly with regard to repolarization amplitudes.) Insofaras temporal parameters are concerned, any of a variety of inter-featureor intra-feature intervals may be tracked, such as, e.g., A-A intervals,AV intervals, R-R intervals, ST intervals, etc. Note that the evaluationof R-R interval is particularly helpful in detecting respiratory sinusarrhythmia (RSA), which is heart rate variability in synchrony withrespiration.

It is important to correctly extract morphological and temporal variablefrom IEGM data to ensure that the variable properly reflects therespiratory modulation. A history of the morphological characteristicscan be used to aid in variable extraction. FIG. 20 illustrates anexemplary cardiac cycle 809 including an R-wave (i.e. QRS complex) and aT-wave. Brackets illustrate a historical range of values for T-waveparameters such as T-wave amplitude and ST-interval. Historical rangessuch as those illustrated in FIG. 20 may be used by the pacer/ICD toproperly identify the parameter to be extract from the IEGM. Forexample, use of the T-wave amplitude range can be used by the pacer/ICDto correctly extract T-wave amplitude values.

At step 810, the pacer/ICD examines the temporal and morphologicalparameters to detect any fused beats and to reject the parametersderived from fused beats, as these parameters may be anomalous. In oneexample, various sets of histogram bins are stored in memory and used totrack the distribution of parameter values. Each memory bin isassociated with a range of values. An exemplary set of bins for use withvPDI is illustrated in FIG. 21. Whenever, a new vPDI value is detected,the pacer/ICD increments the bin corresponding to the new value. Thehistogram bins thereby quantify the distribution of vPDI values over aperiod of time. In the specific example of FIG. 21, exemplary vPDI binvalues 812 obtained over a six hour period of time are illustrated. (Thevertical axis represents bin count. The horizontal axis represents PDIvalues along an arbitrary scale.) As can be seen, the majority ofindividual cardiac cycles have vPDI values in the range of 600 and 1200.Any vPDI value that deviates significantly from that range is likely theresult of a fused beat and is preferably discarded. Depending upon theimplementation, a set of histogram bins such as illustrated in FIG. 21may be maintained for each temporal and morphological parameter. Inother implementations, histogram bins are maintained only for selectedcardiac cycle parameters. Otherwise routine experimentation may beemployed to identify particular parameters that are advantageouslytracked via histogram bins. Preferably, the parameters that are trackedvia histogram bins are parameters that are strongly affected by fusionso as to permit fused beat to be readily detected and rejected.Parameters such as vPDI, aPDI, tI, ffPDI, and fftI are typically goodcandidates. Other techniques for detecting and rejecting fused beats mayadditionally or alternatively be employed.

At steps 814 and 816 of FIG. 19, the pacer/ICD determines baselinevalues for the temporal and morphological parameters and corrects forany baseline shifts due to changes in posture. In one example, separaterunning averages for each of the parameters are tracked. The runningaverage is used as current baseline. Parameters are corrected bysubtracting the baseline from the values. Any significant drift in therunning average is indicative of a change in posture or other change notdue to respiratory variations. Other techniques may be used fordetecting changes in posture such as techniques set forth in U.S. patentapplication Ser. No. 10/329,233, of Koh et al., entitled “System andMethod for Determining Patient Posture Based On 3-D Trajectory Using anImplantable Medical Device”, filed Dec. 23, 2002. Once a change inposture is detected, a new baseline value is calculated based on arunning average of values of the parameter obtained after the change inposture. Preferably, all parameters used in detecting individualrespiratory cycles for the purposes of detecting abnormal respirationare corrected for baseline shifts using there or other suitablecorrection techniques.

At step 818, the pacer/ICD analyzes the corrected temporal andmorphological parameters to identify individual respiratory cycles (i.e.breaths) based on cyclical changes therein. As already explained inconnection with FIGS. 2-13, the temporal and morphological parametersderived from the IEGM exhibit cyclical variations indicative ofrespiration. Hence, individual respiratory cycles (i.e. individualbreaths) can be detected based on the correct temporal and morphologicalparameters. Otherwise conventional signal processing techniques may beused to identify cyclical changes in the corrected parameters that arerepresentative of respiration.

FIG. 22 illustrates cyclical variations in tPDI (i.e. the paceddepolarization integral associated with a T-wave) detected by apacer/ICD along with externally derived signals representing patientrespiration including: nasal flow 822, chest expansion 824, andabdominal expansion 826. As can be seen, the tPDI signal exhibitscyclical variations that are correlated with patient respiration. Thesecyclical variations are detected to identify the individual respiratorycycles at step 818 of FIG. 19. Note that the particular respiratorypattern shown in FIG. 22 exhibits CSR. The tPDI signal not only permitsthe individual respiratory cycles to be detected but also permits CSR tobe detected, as will be described below with reference to FIGS. 25 and26. Note also that FIG. 22 additionally illustrates a power value 828associated with the tPDI signal, which is helpful in detecting abnormalrespiration and will also be described below.

Exemplary techniques for detecting respiratory parameters will now bedescribed with respect to FIGS. 23-24. These techniques may be used atstep 802 of FIG. 18. At step 830, the pacer/ICD examines the individualrespiratory cycles detected via the procedure of FIG. 19 to detectinter-breath intervals, i.e. the interval between peaks of consecutiverespiratory cycles. At step 832, the pacer/ICD then integrates therespiration pattern over each individual inter-breath interval todetermine depth of respiration for each individual respiratory cycle.The respiratory pattern that is integrated is the signal from which therespiratory cycles were derived and hence can depend on the particulartemporal or morphological parameters detected by the device. In theexample of FIG. 22, where tPDI is used to detect respiratory cycles, thetPDI signal is integrated over each respiratory cycle found therein.FIG. 24 illustrates an example where a vPDI signal 834 is insteaddetected during a period of CSR. In that example, the vPDI signal isintegrated over each inter-breath interval found therein to obtain arespiration depth signal 835. A single respiration depth value isobtained for each respiratory cycle. Otherwise conventional numericalintegration techniques can be employed by the pacer/ICD. For comparisonpurposes, FIG. 24 also illustrates externally-derived respiratorysignals: a chest signal 836, an abdominal signal 838 and a nasal flowsignal 840, each tracking the periodic CSR pattern. As can be seen, therespiration depth signal 835 derived by integrating the vPDI signal 834also exhibits with the CSR cyclical variations found in theexternally-derived signals. Accordingly, respiration depth, by itself,can typically be used to detect abnormal respiration. However, toenhance reliability and specificity, the pacer/ICD preferably determinesvarious other respiratory parameters.

At step 842 of FIG. 23, the pacer/ICD evaluates the depth of respirationover multiple respiratory cycles to determine its standard deviation andmedian. Otherwise conventional statistical techniques may be employed toevaluate the standard deviation and the median. Other statistical valuesmay additionally or alternatively be evaluated. At step 844, thepacer/ICD determines “respiratory power” by summing respiration depth(835 of FIG. 24) over discrete temporal epochs. The epochs arepreferably ten seconds each, but other durations may be used for theepochs such as values in the range of five to fifteen seconds. Hence, inone example, once every ten seconds the pacer/ICD sums the respirationdepth values obtained during the previous ten second epoch. Anexemplary, resulting respiratory power signal 846 is also shown in FIG.24. It too exhibits cyclical variations indicative of CSR. Note,however, because the respiratory power is derived based on the precedingten seconds of respiratory depth signals, its cyclical variations tendto lag those exhibited in the respiration depth signal. At step 848, thepacer/ICD stores the various values it has calculated: depth ofrespiration, standard deviation of the depth of respiration, median ofdepth of respiration, and power of respiration for use in detectingabnormal respiration and for subsequent diagnostic review. In additionto calculating a power value from the respiration depth, the pacer/ICDcan also calculate a power value associated with each of the temporaland morphological parameters (such as iPDI, vPDI, etc.) by integratingthese parameters over the last ten second epoch. (This is discussedfurther with reference to FIG. 27.) The additional power values may becombined with the respiration power to provide a more robust power valuefor use in abnormal respiration detection.

Turning now to FIG. 25, exemplary techniques for detecting significantchanges in the respiratory parameters will be described for use at step804 of FIG. 18. At step 850, the pacer/ICD detects gradual and/or abruptchanges in the respiratory parameters, i.e. in one or more of depth ofrespiration, standard deviation of the depth of respiration, median ofdepth of respiration, and power of respiration. Otherwise conventionalsignal processing techniques can be used to detect changes, such asevaluating a time derivative of the signal, which represents a rate ofchange of the signal with time. The faster the rate of change, the moreabrupt the change in the parameter. Depending upon the implementation,it may be desirable to combine two or more respiratory parameters into asignal for evaluation. As noted, however, the respiratory power signallags the other signals and hence care should be taken if combining thepower signal with the other signals. In any case, at step 852, thepacer/ICD quantifies the magnitude of the change. In one example, themagnitude of the change is simply the post-change value of therespiratory parameters (or combination of parameters) minus thepre-change value.

FIG. 26 illustrates a technique for evaluating the changes to therespiratory parameters to detect abnormal respiration for use at step806 of FIG. 18. Ate step 854, the pacer/ICD compares the magnitude ofthe changes against one or more thresholds indicative of abnormalrespiration. For example, if the respiratory parameter being tracked isthe depth of respiration, the magnitude of any abrupt change inrespiration depth is compared against a predetermined thresholdindicative of abnormal respiration. The thresholds depend on theparticular parameter being tracked and may further vary from patient topatient. Suitable threshold values may be specified following implant ofdevice based on the specific characteristics of patient in which thedevice is implanted and/or may be automatically updated during routineworking of the algorithm. The direction of the change is also preferablyanalyzed to determine whether an episode of abnormal respiration isbeginning or ending. For example, a sudden drop in respiration depthfrom a previously normal depth is indicative of the onset ofapnea/hypopnea. A sudden increase in respiration depth from a previouslyabnormally low respiration depth is instead indicative of a return tonormal respiration. A still further abrupt increase may be indicative ofthe onset of hyperpnea. At step 856, the pacer/ICD then generatessignals indicative of onset or termination of abnormal respiration. Thesignals may be used to trigger warning signals or to control therapy, asalready described above with reference to FIG. 14. Also, as has alreadybeen explained in connection with FIG. 14, the pacer/ICD preferablyidentifies the particular form of abnormal respiration so as to permitthe appropriate therapy and/or warnings to be delivered.

FIG. 27 further illustrates and summarizes the components and methodsteps employed to implement the technique of FIGS. 18-26. As will beexplained, the technique of FIG. 27 includes some additional features aswell. Briefly, raw IEGM data 900 is filtered via sense amplifiers 902 toyield sense amplifier data 904. The sense amplifier data is analyzed viaan event identification block 906 to identify and extract individualevents 908, such as paced atrial or ventricular events and intrinsic(i.e. sensed) atrial and ventricular events. These steps/components areconventional. Then, the raw IEGM data and the events found therein areprocessed at block 910 to extract variables for the purposes of abnormalrespiration detection. That is, data and signal from all available IEGMchannels of cardiac rhythm management (CRM) device, i.e. the pacer/CD,are extracted. Examples include: R-R, P-P, PDI, QT interval, aPDI, vPDI,ffPDI, tI, fftI etc. At block 912, the pacer/CD derives or calculatesdistributions for the various variables. The histogram techniquesdescribed above with reference to FIG. 21 may be employed to derive thedistributions. At block 914, the pacer/CD calculates and removesrespective baseline means, preferably using techniques described abovewith reference to steps 814 an 816 of FIG. 19. At block 915, thepacer/ICD preferably normalizes the variables to remove bodyposition-based variability. In one example, a running average of themean and the standard-deviation are maintained with a window of widthequal to 64 beats, i.e. i−64 to i+64. An i+64 scheme is preferable tousing an i−128 to i scheme to prevent lag due to such calculation. TheIEGM variables are then normalized by eliminating any variation in thecalculated mean and standard-deviation. In one specific example, thefollowing formula is used:

$x_{i}^{1} = {{\frac{x_{i} - \mu}{\sigma}i} \in N}$

where □=local mean, and where the local standard deviations are used. Inthis manner, a moving window normalization is achieved to remove themean and standard-deviation changes that occur due to body positionchanges while retaining the relative variations due to respiration.

At block 916, the pacer/CD calculates the power of each variable over aprevious epoch of X seconds (e.g. 10 seconds.) This is performed byintegrating the variable over the previous epoch of time. These powervalues are in addition to the respiration power value that is describedabove with reference to FIG. 23 and provide a further set of values toaid in detecting abnormal respiration. At block 918, the pacer/ICDevaluates the breathing interval, i.e. the inter-breath interval orinterval between peaks of consecutive respiratory cycles. Once thebreathing interval is calculated, the pacer/ICD can then evaluaterespiratory power, which is a sum of respiration depth over thepreceding epoch of time where a single respiration depth value iscalculated for each individual breathing interval.

At black 922, the pacer/ICD compares the various derived parameters andone or more thresholds, as described above with reference to FIGS. 25and 26 to detect abnormal respiration. At block 924, individual events(i.e. individual episodes of abnormal respiration) are detected andappropriate diagnostics are stored. Also, as already explained,appropriate therapy may be applied and/or warning signals may begenerated.

The techniques of FIGS. 18-27 are advantageously performed while thepatient is asleep to detect nocturnal forms of abnormal respiration, butmay potentially be performed while awake. (CSR, for example, can occurwhile a patient is awake, particularly if CHF is severe.) Inimplementations wherein the techniques are implemented only while thepatient is asleep, otherwise conventional sleep detection techniques maybe used to detect the onset and termination of sleep. In addition, thesleep detection techniques of the Bornzin et al. and Park et al.,patents, cited above, may be used. Additionally, or in the alternative,respiratory parameters detected by the techniques described herein—suchas depth of respiration—may be used to detect sleep. In this regard,sleep, compared with wakefulness is associated with reduced lungventilation mainly due to decreases in tidal volume. Accordingly,detection of persistent low respiration depth can be used alone, or incombination with other techniques, to detect sleep. (A respirationprofile incorporating a period of sleep is illustrated in FIG. 15 anddescribed above.)

Also, whereas the techniques of FIGS. 18-27 are advantageously employedin “real time” (based on IEGM signals as they are sensed), the techniquecan alternatively be employed based on previously recorded parameters.For example, data may be collected overnight than analyzed later todetect episodes of abnormal respiration that have already occurred forthe purpose of generate appropriate diagnostic data for physicianreview.

What have been described are various systems and methods for trackingrespiration, detecting episodes of abnormal respiration and deliveringtherapy in response thereto using an implantable system controlled by apacer or ICD. However, principles of the invention may be exploitingusing other implantable systems or in accordance with other techniques.Thus, while the invention has been described with reference toparticular exemplary embodiments, modifications can be made theretowithout departing from the scope of the invention.

1. A method for detecting abnormal respiration within a patient using animplantable medical device, the method comprising: sensing cardiacelectrical signals, identifying individual cardiac cycles therein, andidentifying one or more selected electrical events within the individualcardiac cycles; detecting one or more parameters associated withindividual selected electrical events of the cardiac cycle and detectingpatient respiration based on cycle-to-cycle changes in the detectedparameters associated with the individual selected electrical events;and detecting abnormal respiration by identifying individual respiratorycycles within the patient respiration, detecting one or more parametersassociated with the individual respiratory cycles, including aninter-breath interval and a depth of respiration, detecting anysignificant changes in the parameters associated with the individualrespiratory cycles, and evaluating the significant changes to detectabnormal respiration.
 2. The method of claim 1 wherein the cardiacelectrical signals are derived from one or more of unipolar leads andbipolar leads.
 3. The method of claim 1 wherein the electrical eventsinclude depolarization events.
 4. The method of claim 3 wherein thedepolarization events include one or more of intrinsic atrialdepolarization events (P-waves), atrial evoked responses (AER),intrinsic ventricular depolarization events (QRS-complexes), ventricularevoked responses (VER) and premature ventricular events (PVEs).
 5. Themethod of claim 1 wherein the electrical events include repolarizationevents.
 6. The method of claim 1 wherein the parameters associated withthe electrical events comprise one or more of maximum amplitude,peak-to-peak amplitude, width and integral.
 7. The method of claim 6wherein the integral is a paced depolarization integral (PDI).
 8. Themethod of claim 1 and further comprising: detecting one or moreintervals associated with combinations of electrical events; and whereindetecting patient respiration comprises processing a combination ofcycle-to-cycle changes in the detected intervals and cycle-to-cyclechanges in the detected parameters.
 9. The method of claim 1 whereindetecting patient respiration based on cycle-to-cycle changes in thedetected parameters associated with the individual selected electricalevents includes identifying baselines associated with the detectedparameters; and correcting for changes in the baselines.
 10. The methodof claim 1 wherein identifying individual respiratory cycles withinpatient respiration comprises detecting cyclical variations in a patientrespiration pattern representative of individual breaths.
 11. The methodof claim 10 wherein detecting one or more parameters associated with theindividual respiratory cycles further includes detecting a standarddeviation of the depth of respiration, and a median of depth ofrespiration.
 12. The method of claim 11 wherein detecting the depth ofrespiration includes calculating a value representative of an integralof patient respiration during individual respiratory cycles.
 13. Themethod of claim 11 further including detecting at least one parameterassociated with multiple respiratory cycles.
 14. The method of claim 13wherein the parameter associated with multiple respiratory cyclesrepresents power of respiration.
 15. The method of claim 1 whereindetecting significant changes in the parameters associated with theindividual respiratory cycles includes detecting one or more of gradualor abrupt changes.
 16. The method of claim 1 wherein the abnormalrespiration includes one or more of apnea, hypopnea, hyperpnea, asthma,Cheyne-Stokes Respiration (CSR).
 17. The method of claim 1 furthercomprising delivering therapy upon detection of an episode of abnormalrespiration.
 18. A system for detecting respiration within a patient foruse within an implantable medical device, the system comprising: sensingcircuitry operative to sense intracardiac electrogram (IEGM) signals; anIEGM-based respiration detector operative to detect features associatedwith individual electrical events within the electrical cardiac signalsand to detect patient respiration based on cycle-to-cycle changes in thefeatures of the individual events; and an IEGM-based abnormalrespiration detector operative to identifying individual respiratorycycles within the patient respiration, detect one or more parametersassociated with the individual respiratory cycles including aninter-breath interval and a depth of respiration, detect any significantchanges in the parameters associated with the individual respiratorycycles, and evaluating the significant changes to detect abnormalrespiration.
 19. An implantable system comprising: means for sensingelectrical cardiac signals; means for detecting patient respirationbased on cycle-to-cycle changes in the electrical cardiac signals; meansfor identifying individual respiratory cycles within the patientrespiration; means for detecting parameters associated with individualrespiratory cycles including an inter-breath interval and a depth ofrespiration; and means for detecting abnormal respiration based onchanges in parameters associated with individual respiratory cycles.